Data Processing, Analysis and Modeling Unit

项目概要/摘要：数据分析单元 华盛顿大学人类肿瘤图谱研究的数据分析单位的超额目标 中心（WU-HTARC）是提供生物信息学工具和处理/分析基础设施， 分析表征股生成的数据。最重要的是，我们将整合数据， 方法学（组学/成像/表型分析）和维度（1D/2D/3D/时间）谱 为三种癌症类型中的每一种：GBM，PDAC和BRCA/TNBC提供连贯和可访问的肿瘤图谱。 在基本层面上，每个地图集将首先包括对细胞的各种数值计算指标进行编目 类型，包括单个细胞类型的分数（1D）、密度、分散度和位置测量， 不同细胞类型（例如免疫细胞和肿瘤细胞）的空间散布性的欧几里得度量（2D和3D）。 3D），以及这些指标如何随时间变化。然后，我们将这些信息与基因组 分析，如突变特征、克隆性和显著突变的基因/区域/途径、蛋白质组学 代谢组学分析和图像数据。地图集的核心将是一个MySQL关系 数据库，不仅存储所有收集的数据，但链接他们沿沿着这些不同的维度。用户将 通过一个先进的基于浏览器的查看器/查询门户网站与地图集接口， 传统的基于文本的查询以及基于空间的查询（形状、特征位置等）。凝聚力 在这三个地图集中，就使用的数据范围和它们的构建方法而言， 允许用户生成现在不可能的新类型的假设，并执行泛癌症分析， 揭示了三种代表性实体瘤的共性和差异，并可能外推 将这些发现应用于其他肿瘤。

Project Summary/Abstract: Data Analysis Unit The over-arching goal of the Data Analysis Unit for the Washington University Human Tumor Atlas Research Center (WU-HTARC) is to provide bioinformatics tools and processing/analysis infrastructure for in-depth analyses of the data generated in the Characterization Unit. Most importantly, we will integrate data across both the methodological (omics/imaging/phenotypic analyses) and the dimensional (1D/2D/3D/time) spectrums into coherent and accessible tumor atlases for each of the three cancer types: GBM, PDAC, and BRCA/TNBC. At the basic level, each atlas will consist of first cataloging a variety of numerically-computed metrics for cell types, including fractions (1D), density, dispersion, and location measures for individual cell types and Euclidean measures of spatial interspersedness of different cell types, e.g. immune and tumor cells (2D and 3D), and how these metrics change with time. We will then correlate this information with both genomic analyses, such as mutation signatures, clonality, and significantly mutated genes/regions/pathways, proteomic and metabolomics analyses, and image-derived data. The core of the atlas will be a MySQL relational database that not only stores all collected data, but links them along these different dimensions. Users will interface with the atlas through a sophisticated viewer/query browser-based web portal that will support both traditional text-based queries, as well as spatial-based queries (shape, feature locations, etc.). The cohesion among the three atlases, in terms of the spectrum of data used and the approaches of their construction, will allow users to generate new types of hypotheses not now possible and to perform pan-cancer analyses to reveal commonalities and differences in the three representative solid tumors, and to potentially extrapolate these findings to other tumors.